{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"07_Logistic_Regression","provenance":[{"file_id":"https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/07_Logistic_Regression.ipynb","timestamp":1608040661018},{"file_id":"https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/07_Logistic_Regression.ipynb","timestamp":1582817842854}],"collapsed_sections":[],"toc_visible":true},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"markdown","metadata":{"id":"zmbTeI3g2SH3"},"source":["<div align=\"center\">\n","<h1><img width=\"30\" src=\"https://madewithml.com/static/images/rounded_logo.png\">&nbsp;<a href=\"https://madewithml.com/\">Made With ML</a></h1>\n","Applied ML · MLOps · Production\n","<br>\n","Join 30K+ developers in learning how to responsibly <a href=\"https://madewithml.com/about/\">deliver value</a> with ML.\n","    <br>\n","</div>\n","\n","<br>\n","\n","<div align=\"center\">\n","    <a target=\"_blank\" href=\"https://newsletter.madewithml.com\"><img src=\"https://img.shields.io/badge/Subscribe-30K-brightgreen\"></a>&nbsp;\n","    <a target=\"_blank\" href=\"https://github.com/GokuMohandas/MadeWithML\"><img src=\"https://img.shields.io/github/stars/GokuMohandas/MadeWithML.svg?style=social&label=Star\"></a>&nbsp;\n","    <a target=\"_blank\" href=\"https://www.linkedin.com/in/goku\"><img src=\"https://img.shields.io/badge/style--5eba00.svg?label=LinkedIn&logo=linkedin&style=social\"></a>&nbsp;\n","    <a target=\"_blank\" href=\"https://twitter.com/GokuMohandas\"><img src=\"https://img.shields.io/twitter/follow/GokuMohandas.svg?label=Follow&style=social\"></a>\n","    <br>\n","    🔥&nbsp; Among the <a href=\"https://github.com/topics/deep-learning\" target=\"_blank\">top ML</a> repositories on GitHub\n","</div>\n","\n","<br>\n","<hr>"]},{"cell_type":"markdown","metadata":{"id":"QsUcHSE0TcRM"},"source":["# Logistic Regression\n","\n","In this lesson, we're going to implement logistic regression for a classification task where we want to probabilistically determine the outcome for a given set of inputs. We will understand the basic math behind it, implement it in just NumPy and then in PyTorch."]},{"cell_type":"markdown","metadata":{"id":"G-CQd9joTxwB"},"source":["<div align=\"left\">\n","<a target=\"_blank\" href=\"https://madewithml.com/courses/foundations/logistic-regression/\"><img src=\"https://img.shields.io/badge/📖 Read-blog post-9cf\"></a>&nbsp;\n","<a href=\"https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/07_Logistic_Regression.ipynb\" role=\"button\"><img src=\"https://img.shields.io/static/v1?label=&amp;message=View%20On%20GitHub&amp;color=586069&amp;logo=github&amp;labelColor=2f363d\"></a>&nbsp;\n","<a href=\"https://colab.research.google.com/github/GokuMohandas/MadeWithML/blob/main/notebooks/07_Logistic_Regression.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n","</div>"]},{"cell_type":"markdown","metadata":{"id":"VoMq0eFRvugb"},"source":["# Overview"]},{"cell_type":"markdown","metadata":{"id":"orlWnRz2-wyO"},"source":["Logistic regression is an extension on linear regression (both are generalized linear methods). We will still learn to model a line (plane) that models $y$ given $X$. Except now we are dealing with classification problems as opposed to regression problems so we'll be predicting probability distributions as opposed to a discrete value. We'll be using the softmax operation to normalize our logits ($XW$) to derive probabilities.\n","\n","Our goal is to learn a logistic model $\\hat{y}$ that models $y$ given $X$. \n","\n","$ \\hat{y} = \\frac{e^{XW_y}}{\\sum_j e^{XW}} $ \n","* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NX1}$ ($N$ is the number of samples)\n","* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n","* $W$ = weights | $\\in \\mathbb{R}^{DXC}$ ($C$ is the number of classes)\n","\n","This function is known as the multinomial logistic regression or the softmax classifier. The softmax classifier will use the linear equation ($z=XW$) and normalize it (using the softmax function) to produce the probabiltiy for class y given the inputs.\n","\n","> We'll leave the bias weights out for now to avoid complicating the backpropagation calculation."]},{"cell_type":"markdown","metadata":{"id":"T4Y55tpzIjOa"},"source":["* **Objective:**  Predict the probability of class $y$ given the inputs $X$. The softmax classifier normalizes the linear outputs to determine class probabilities. \n","* **Advantages:**\n","  * Can predict class probabilities given a set on inputs.\n","* **Disadvantages:**\n","  * Sensitive to outliers since objective is to minimize cross entropy loss. Support vector machines (SVMs) are a good alternative to counter outliers.\n","* **Miscellaneous:** Softmax classifier is going to used widely in neural network architectures as the last layer since it produces class probabilities."]},{"cell_type":"markdown","metadata":{"id":"3SYAo2FHl1I3"},"source":["# Set up"]},{"cell_type":"code","metadata":{"id":"uNdOxyfqF1jn"},"source":["import numpy as np\n","import random"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"wZdhpBZfF1mC"},"source":["SEED = 1234"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"izOKq9r2F1pT"},"source":["# Set seed for reproducibility\n","np.random.seed(SEED)\n","random.seed(SEED)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"00B5uEM8Tb0d"},"source":["## Load data"]},{"cell_type":"markdown","metadata":{"id":"sYNIM3PxQGgo"},"source":["We'll used some synthesized data to train our models on. The task is to determine whether a tumor will be benign (harmless) or malignant (harmful) based on leukocyte (white blood cells) count and blood pressure. Note that this is a synthetic dataset that has no clinical relevance."]},{"cell_type":"code","metadata":{"id":"A8rspXhNEgrL"},"source":["import matplotlib.pyplot as plt\n","import pandas as pd\n","from pandas.plotting import scatter_matrix"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"7alqmyzXtgE8","colab":{"base_uri":"https://localhost:8080/","height":204},"executionInfo":{"status":"ok","timestamp":1608246146611,"user_tz":420,"elapsed":1925,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"db2e90a2-1e70-4357-bddd-7617129e05e2"},"source":["# Read from CSV to Pandas DataFrame\n","url = \"https://raw.githubusercontent.com/GokuMohandas/MadeWithML/main/datasets/tumors.csv\"\n","df = pd.read_csv(url, header=0) # load\n","df = df.sample(frac=1).reset_index(drop=True) # shuffle\n","df.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>leukocyte_count</th>\n","      <th>blood_pressure</th>\n","      <th>tumor_class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>15.335860</td>\n","      <td>14.637535</td>\n","      <td>benign</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>9.857535</td>\n","      <td>14.518942</td>\n","      <td>malignant</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>17.632579</td>\n","      <td>15.869585</td>\n","      <td>benign</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>18.369174</td>\n","      <td>14.774547</td>\n","      <td>benign</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>14.509367</td>\n","      <td>15.892224</td>\n","      <td>malignant</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   leukocyte_count  blood_pressure tumor_class\n","0        15.335860       14.637535      benign\n","1         9.857535       14.518942   malignant\n","2        17.632579       15.869585      benign\n","3        18.369174       14.774547      benign\n","4        14.509367       15.892224   malignant"]},"metadata":{"tags":[]},"execution_count":4}]},{"cell_type":"code","metadata":{"id":"3aqJxfKmnYTs"},"source":["# Define X and y\n","X = df[['leukocyte_count', 'blood_pressure']].values\n","y = df['tumor_class'].values"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"lpoF3_EgDtPl","colab":{"base_uri":"https://localhost:8080/","height":279},"executionInfo":{"status":"ok","timestamp":1608246147088,"user_tz":420,"elapsed":2386,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"97857d91-3c1d-441a-b07c-1a40ad100d4a"},"source":["# Plot data\n","colors = {'benign': 'red', 'malignant': 'blue'}\n","plt.scatter(X[:, 0], X[:, 1], c=[colors[_y] for _y in y], s=25, edgecolors='k')\n","plt.xlabel('leukocyte count')\n","plt.ylabel('blood pressure')\n","plt.legend(['malignant ', 'benign'], loc=\"upper right\")\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"23rUN1uTpoc4"},"source":["## Split data"]},{"cell_type":"markdown","metadata":{"id":"ujHPintkze4U"},"source":["We want to split our dataset so that each of the three splits has the same distribution of classes so that we can train and evaluate properly. We can easily achieve this by telling scikit-learn's [`train_test_split`](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) function what to `stratify` on. "]},{"cell_type":"code","metadata":{"id":"UrYcWmcggDHb"},"source":["import collections\n","from sklearn.model_selection import train_test_split"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ekgu8AS_zZOs"},"source":["TRAIN_SIZE = 0.7\n","VAL_SIZE = 0.15\n","TEST_SIZE = 0.15"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"27h91LX0pp7F"},"source":["def train_val_test_split(X, y, train_size):\n","    \"\"\"Split dataset into data splits.\"\"\"\n","    X_train, X_, y_train, y_ = train_test_split(X, y, train_size=TRAIN_SIZE, stratify=y)\n","    X_val, X_test, y_val, y_test = train_test_split(X_, y_, train_size=0.5, stratify=y_)\n","    return X_train, X_val, X_test, y_train, y_val, y_test"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"D4fy2vUjr-Qe","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246147958,"user_tz":420,"elapsed":3234,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"14749ee9-1efb-4e5a-d6f0-64a23f660d07"},"source":["# Create data splits\n","X_train, X_val, X_test, y_train, y_val, y_test = train_val_test_split(\n","    X=X, y=y, train_size=TRAIN_SIZE)\n","print (f\"X_train: {X_train.shape}, y_train: {y_train.shape}\")\n","print (f\"X_val: {X_val.shape}, y_val: {y_val.shape}\")\n","print (f\"X_test: {X_test.shape}, y_test: {y_test.shape}\")\n","print (f\"Sample point: {X_train[0]} → {y_train[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X_train: (700, 2), y_train: (700,)\n","X_val: (150, 2), y_val: (150,)\n","X_test: (150, 2), y_test: (150,)\n","Sample point: [14.95081332 14.86441305] → malignant\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"kRlhY__n14bd"},"source":["Now let's see how many samples per class each data split has:"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VPnzBFsd2SvC","executionInfo":{"status":"ok","timestamp":1608246147959,"user_tz":420,"elapsed":3223,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"88328dfa-3396-4323-d396-a3d42b68467c"},"source":["# Overall class distribution\n","class_counts = dict(collections.Counter(y))\n","print (f\"Classes: {class_counts}\")\n","print (f\"m:b = {class_counts['malignant']/class_counts['benign']:.2f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Classes: {'benign': 389, 'malignant': 611}\n","m:b = 1.57\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"exGHJSxq2SzF","executionInfo":{"status":"ok","timestamp":1608246147962,"user_tz":420,"elapsed":3215,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"25ca7304-517e-4d0a-c553-9c044a4ea589"},"source":["# Per data split class distribution\n","train_class_counts = dict(collections.Counter(y_train))\n","val_class_counts = dict(collections.Counter(y_val))\n","test_class_counts = dict(collections.Counter(y_test))\n","print (f\"train m:b = {train_class_counts['malignant']/train_class_counts['benign']:.2f}\")\n","print (f\"val m:b = {val_class_counts['malignant']/val_class_counts['benign']:.2f}\")\n","print (f\"test m:b = {test_class_counts['malignant']/test_class_counts['benign']:.2f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["train m:b = 1.57\n","val m:b = 1.54\n","test m:b = 1.59\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"lLHG5_vpHQ3d"},"source":["## Label encoding"]},{"cell_type":"markdown","metadata":{"id":"YGWO8zDCHTFp"},"source":["You'll notice that our class labels are text. We need to encode them into integers so we can use them in our models. We could scikit-learn's [`LabelEncoder`](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.LabelEncoder.html#sklearn.preprocessing.LabelEncoder) to do this but we're going to write our own simple label encoder class so we can see what's happening under the hood. "]},{"cell_type":"code","metadata":{"id":"t7XSFDGlGQfE"},"source":["import itertools"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KPIsL1kAHkhu"},"source":["class LabelEncoder(object):\n","    \"\"\"Label encoder for tag labels.\"\"\"\n","    def __init__(self, class_to_index={}):\n","        self.class_to_index = class_to_index\n","        self.index_to_class = {v: k for k, v in self.class_to_index.items()}\n","        self.classes = list(self.class_to_index.keys())\n","\n","    def __len__(self):\n","        return len(self.class_to_index)\n","\n","    def __str__(self):\n","        return f\"<LabelEncoder(num_classes={len(self)})>\"\n","\n","    def fit(self, y):\n","        classes = np.unique(y)\n","        for i, class_ in enumerate(classes):\n","            self.class_to_index[class_] = i\n","        self.index_to_class = {v: k for k, v in self.class_to_index.items()}\n","        self.classes = list(self.class_to_index.keys())\n","        return self\n","\n","    def encode(self, y):\n","        encoded = np.zeros((len(y)), dtype=int)\n","        for i, item in enumerate(y):\n","            encoded[i] = self.class_to_index[item]\n","        return encoded\n","\n","    def decode(self, y):\n","        classes = []\n","        for i, item in enumerate(y):\n","            classes.append(self.index_to_class[item])\n","        return classes\n","\n","    def save(self, fp):\n","        with open(fp, 'w') as fp:\n","            contents = {'class_to_index': self.class_to_index}\n","            json.dump(contents, fp, indent=4, sort_keys=False)\n","\n","    @classmethod\n","    def load(cls, fp):\n","        with open(fp, 'r') as fp:\n","            kwargs = json.load(fp=fp)\n","        return cls(**kwargs)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hcfHtYFtHkmo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246147964,"user_tz":420,"elapsed":3202,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"41944bd3-c546-43e7-bcc4-57b0abaed7f8"},"source":["# Fit\n","label_encoder = LabelEncoder()\n","label_encoder.fit(y_train)\n","label_encoder.class_to_index"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'benign': 0, 'malignant': 1}"]},"metadata":{"tags":[]},"execution_count":15}]},{"cell_type":"code","metadata":{"id":"4hbPtvg4HoqE","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246147964,"user_tz":420,"elapsed":3190,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"a9a67cab-4ad8-4fba-e972-573986a49476"},"source":["# Encoder\n","print (f\"y_train[0]: {y_train[0]}\")\n","y_train = label_encoder.encode(y_train)\n","y_val = label_encoder.encode(y_val)\n","y_test = label_encoder.encode(y_test)\n","print (f\"y_train[0]: {y_train[0]}\")\n","print (f\"decoded: {label_encoder.decode([y_train[0]])}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["y_train[0]: malignant\n","y_train[0]: 1\n","decoded: ['malignant']\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"nkAmzEwVHs6E"},"source":["We also want to calculate our class weights, which are useful for weighting the loss function during training. It tells the model to focus on samples from an under-represented class. The loss section below will show how to incorporate these weights."]},{"cell_type":"code","metadata":{"id":"U7MTxcrcHotG","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246147965,"user_tz":420,"elapsed":3179,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"2e41f8d4-02d9-412b-d59e-74b3020cf126"},"source":["# Class weights\n","counts = np.bincount(y_train)\n","class_weights = {i: 1.0/count for i, count in enumerate(counts)}\n","print (f\"counts: {counts}\\nweights: {class_weights}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["counts: [272 428]\n","weights: {0: 0.003676470588235294, 1: 0.002336448598130841}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"IodMyhnAJUg_"},"source":["## Standardize data"]},{"cell_type":"markdown","metadata":{"id":"QRhNHi9YJfDm"},"source":["We need to standardize our data (zero mean and unit variance) so a specific feature's magnitude doesn't affect how the model learns its weights. We're only going to standardize the inputs X because our outputs y are class values."]},{"cell_type":"code","metadata":{"id":"rjIz1KKYJWI7"},"source":["from sklearn.preprocessing import StandardScaler"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hP6VTXSiJWM1"},"source":["# Standardize the data (mean=0, std=1) using training data\n","X_scaler = StandardScaler().fit(X_train)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"kn2SDd28Jwna"},"source":["# Apply scaler on training and test data (don't standardize outputs for classification)\n","X_train = X_scaler.transform(X_train)\n","X_val = X_scaler.transform(X_val)\n","X_test = X_scaler.transform(X_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"zBAu7GkJJwq8","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246147967,"user_tz":420,"elapsed":3161,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"714bd0bf-92ba-49dc-9b92-2ed2437aaae9"},"source":["# Check (means should be ~0 and std should be ~1)\n","print (f\"X_test[0]: mean: {np.mean(X_test[:, 0], axis=0):.1f}, std: {np.std(X_test[:, 0], axis=0):.1f}\")\n","print (f\"X_test[1]: mean: {np.mean(X_test[:, 1], axis=0):.1f}, std: {np.std(X_test[:, 1], axis=0):.1f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["X_test[0]: mean: -0.1, std: 1.0\n","X_test[1]: mean: -0.1, std: 1.0\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"CYH7kRTd9WoJ"},"source":["# NumPy\n","\n","Now that we have our data prepared, we'll first implement logistic regression using just NumPy. This will let us really understand the underlying operations. It's normal to find the math and code in this section slightly complex. You can still read each of the steps to build intuition for when we implement this using PyTorch."]},{"cell_type":"markdown","metadata":{"id":"Y-gUbD71-yiX"},"source":["Our goal is to learn a logistic model $\\hat{y}$ that models $y$ given $X$. \n","\n","$ \\hat{y} = \\frac{e^{XW_y}}{\\sum e^{XW}} $ \n","* $\\hat{y}$ = prediction | $\\in \\mathbb{R}^{NX1}$ ($N$ is the number of samples)\n","* $X$ = inputs | $\\in \\mathbb{R}^{NXD}$ ($D$ is the number of features)\n","* $W$ = weights | $\\in \\mathbb{R}^{DXC}$ ($C$ is the number of classes)"]},{"cell_type":"markdown","metadata":{"id":"6wcc5lbdH_Hi"},"source":["> We are going to use multinomial logistic regression even though our task only involves two classes because you can generalize the softmax classifier to any number of classes."]},{"cell_type":"markdown","metadata":{"id":"YOTrUTpwWWba"},"source":["## Initialize weights"]},{"cell_type":"markdown","metadata":{"id":"o-Os5f8mad5_"},"source":["1. Randomly initialize the model's weights $W$."]},{"cell_type":"code","metadata":{"id":"jdPe3f01Wg6X"},"source":["INPUT_DIM = X_train.shape[1] # X is 2-dimensional\n","NUM_CLASSES = len(label_encoder.classes) # y has two possibilities (begign or malignant)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6x4heep29c_F","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246147968,"user_tz":420,"elapsed":3147,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"7caec0c2-bdb8-4679-f302-e06c24300990"},"source":["# Initialize random weights\n","W = 0.01 * np.random.randn(INPUT_DIM, NUM_CLASSES)\n","b = np.zeros((1, NUM_CLASSES))\n","print (f\"W: {W.shape}\")\n","print (f\"b: {b.shape}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["W: (2, 2)\n","b: (1, 2)\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"FdnkMO9AWsNB"},"source":["## Model"]},{"cell_type":"markdown","metadata":{"id":"7Fh8YOJjae28"},"source":["2. Feed inputs $X$ into the model to receive the logits ($z=XW$). Apply the softmax operation on the logits to get the class probabilies $\\hat{y}$ in one-hot encoded form. For example, if there are three classes, the predicted class probabilities could look like [0.3, 0.3, 0.4]. \n","  * $ \\hat{y} = softmax(z) = softmax(XW) = \\frac{e^{XW_y}}{\\sum_j e^{XW}} $"]},{"cell_type":"code","metadata":{"id":"EXQkZOwW-otw","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246147969,"user_tz":420,"elapsed":3136,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"2fe4af4d-4e11-4482-b85f-413198d4b711"},"source":["# Forward pass [NX2] · [2X2] + [1,2] = [NX2]\n","logits = np.dot(X_train, W) + b\n","print (f\"logits: {logits.shape}\")\n","print (f\"sample: {logits[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["logits: (700, 2)\n","sample: [-0.0069945   0.00647147]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Ak9FfYIJ-ozJ","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246147970,"user_tz":420,"elapsed":3124,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"95587e4a-94cf-42c1-a303-83b850b3ecc5"},"source":["# Normalization via softmax to obtain class probabilities\n","exp_logits = np.exp(logits)\n","y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n","print (f\"y_hat: {y_hat.shape}\")\n","print (f\"sample: {y_hat[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["y_hat: (700, 2)\n","sample: [0.49663356 0.50336644]\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"qQ39Xr8QW22V"},"source":["## Loss"]},{"cell_type":"markdown","metadata":{"id":"VxxORg12amXv"},"source":[" 3. Compare the predictions $\\hat{y}$ (ex.  [0.3, 0.3, 0.4]) with the actual target values $y$ (ex. class 2 would look like [0, 0, 1]) with the objective (cost) function to determine loss $J$. A common objective function for logistics regression is cross-entropy loss. \n","\n","  * $J(\\theta) = - \\sum_i ln(\\hat{y_i}) = - \\sum_i ln (\\frac{e^{X_iW_y}}{\\sum_j e^{X_iW}}) $"]},{"cell_type":"code","metadata":{"id":"_dfuMmW2Y01f","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246147970,"user_tz":420,"elapsed":3112,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"8a8f26f9-d300-41e4-da4c-899c07babf01"},"source":["# Loss\n","correct_class_logprobs = -np.log(y_hat[range(len(y_hat)), y_train])\n","loss = np.sum(correct_class_logprobs) / len(y_train)\n","print (f\"loss: {loss:.2f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["loss: 0.70\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"iIz5JF8rW5uM"},"source":["## Gradients"]},{"cell_type":"markdown","metadata":{"id":"g9nWUa-3b2lw"},"source":["4. Calculate the gradient of loss $J(\\theta)$ w.r.t to the model weights. Let's assume that our classes are mutually exclusive (a set of inputs could only belong to one class).\n"," * $\\frac{\\partial{J}}{\\partial{W_j}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_j}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_j}} = - \\frac{1}{\\frac{e^{XW_y}}{\\sum_j e^{XW}}}\\frac{\\sum_j e^{XW}e^{XW_y}0 - e^{XW_y}e^{XW_j}X}{(\\sum_j e^{XW})^2} = \\frac{Xe^{XW_j}}{\\sum_j e^{XW}} = X\\hat{y}$\n","  * $\\frac{\\partial{J}}{\\partial{W_y}} = \\frac{\\partial{J}}{\\partial{\\hat{y}}}\\frac{\\partial{\\hat{y}}}{\\partial{W_y}} = - \\frac{1}{\\hat{y}}\\frac{\\partial{\\hat{y}}}{\\partial{W_y}} = - \\frac{1}{\\frac{e^{XW_y}}{\\sum_j e^{XW}}}\\frac{\\sum_j e^{XW}e^{XW_y}X - e^{W_yX}e^{XW_y}X}{(\\sum_j e^{XW})^2} = \\frac{1}{\\hat{y}}(X\\hat{y} - X\\hat{y}^2) = X(\\hat{y}-1)$"]},{"cell_type":"code","metadata":{"id":"9_-LGJWwZqhW"},"source":["# Backpropagation\n","dscores = y_hat\n","dscores[range(len(y_hat)), y_train] -= 1\n","dscores /= len(y_train)\n","dW = np.dot(X_train.T, dscores)\n","db = np.sum(dscores, axis=0, keepdims=True)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Fuw-7BVrW-_M"},"source":["## Update weights"]},{"cell_type":"markdown","metadata":{"id":"QaOhLrqib7t9"},"source":["5. Update the weights $ W $  using a small learning rate $ \\alpha $. The updates will penalize the probability for the incorrect classes (j) and encourage a higher probability for the correct class (y).\n","  * $W_j = W_j - \\alpha\\frac{\\partial{J}}{\\partial{W_j}}$"]},{"cell_type":"code","metadata":{"id":"8pDGJRmQXC39"},"source":["LEARNING_RATE = 1e-1"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"qy6LjHNPZqe1"},"source":["# Update weights\n","W += -LEARNING_RATE * dW\n","b += -LEARNING_RATE * db"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"TsxBN1l8XUVn"},"source":["## Training"]},{"cell_type":"markdown","metadata":{"id":"AFjnVvCqb_Kk"},"source":["6. Repeat steps 2 - 5 to minimize the loss and train the model."]},{"cell_type":"code","metadata":{"id":"wa9N6rLtXVYB"},"source":["NUM_EPOCHS = 50"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"aUh575cGP0Ge"},"source":["# Initialize random weights\n","W = 0.01 * np.random.randn(INPUT_DIM, NUM_CLASSES)\n","b = np.zeros((1, NUM_CLASSES))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"vCipe3EhZqbl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246148533,"user_tz":420,"elapsed":3650,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"c436e4f2-499f-4ede-ad37-fa5dcc4519c4"},"source":["# Training loop\n","for epoch_num in range(NUM_EPOCHS):\n","\n","    # Forward pass [NX2] · [2X2] = [NX2]\n","    logits = np.dot(X_train, W) + b\n","    \n","    # Normalization via softmax to obtain class probabilities\n","    exp_logits = np.exp(logits)\n","    y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n","\n","    # Loss\n","    correct_class_logprobs = -np.log(y_hat[range(len(y_hat)), y_train])\n","    loss = np.sum(correct_class_logprobs) / len(y_train)\n","\n","    # show progress\n","    if epoch_num%10 == 0:\n","        # Accuracy\n","        y_pred = np.argmax(logits, axis=1)\n","        accuracy =  np.mean(np.equal(y_train, y_pred))\n","        print (f\"Epoch: {epoch_num}, loss: {loss:.3f}, accuracy: {accuracy:.3f}\")\n","\n","    # Backpropagation\n","    dscores = y_hat\n","    dscores[range(len(y_hat)), y_train] -= 1\n","    dscores /= len(y_train)\n","    dW = np.dot(X_train.T, dscores)\n","    db = np.sum(dscores, axis=0, keepdims=True)\n","\n","    # Update weights\n","    W += -LEARNING_RATE * dW\n","    b += -LEARNING_RATE * db"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch: 0, loss: 0.684, accuracy: 0.897\n","Epoch: 10, loss: 0.446, accuracy: 0.970\n","Epoch: 20, loss: 0.349, accuracy: 0.973\n","Epoch: 30, loss: 0.297, accuracy: 0.973\n","Epoch: 40, loss: 0.263, accuracy: 0.973\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"EEcyevNxQO36"},"source":["## Evaluation"]},{"cell_type":"markdown","metadata":{"id":"2zHB-mHA1-Uh"},"source":["Now we're ready to evaluate our trained model on our test (hold-out) data split."]},{"cell_type":"code","metadata":{"id":"TpE3oDpIhhBH"},"source":["class LogisticRegressionFromScratch():\n","    def predict(self, x):\n","        logits = np.dot(x, W) + b\n","        exp_logits = np.exp(logits)\n","        y_hat = exp_logits / np.sum(exp_logits, axis=1, keepdims=True)\n","        return y_hat"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-6T8rm1bw_Sw"},"source":["# Evaluation\n","model = LogisticRegressionFromScratch()\n","logits_train = model.predict(X_train)\n","pred_train = np.argmax(logits_train, axis=1)\n","logits_test = model.predict(X_test)\n","pred_test = np.argmax(logits_test, axis=1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Smy3DxJazGFm","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246148535,"user_tz":420,"elapsed":3633,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"4516f2ba-9169-4461-8954-6273e88502b9"},"source":["# Training and test accuracy\n","train_acc =  np.mean(np.equal(y_train, pred_train))\n","test_acc = np.mean(np.equal(y_test, pred_test))\n","print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["train acc: 0.97, test acc: 0.97\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"Cv-Dxp4AxeWh"},"source":["def plot_multiclass_decision_boundary(model, X, y, savefig_fp=None):\n","    \"\"\"Plot the multiclass decision boundary for a model that accepts 2D inputs.\n","    Credit: https://cs231n.github.io/neural-networks-case-study/\n","\n","    Arguments:\n","        model {function} -- trained model with function model.predict(x_in).\n","        X {numpy.ndarray} -- 2D inputs with shape (N, 2).\n","        y {numpy.ndarray} -- 1D outputs with shape (N,).\n","    \"\"\"\n","    # Axis boundaries\n","    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n","    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n","    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101),\n","                         np.linspace(y_min, y_max, 101))\n","\n","    # Create predictions\n","    x_in = np.c_[xx.ravel(), yy.ravel()]\n","    y_pred = model.predict(x_in)\n","    y_pred = np.argmax(y_pred, axis=1).reshape(xx.shape)\n","\n","    # Plot decision boundary\n","    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n","    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n","    plt.xlim(xx.min(), xx.max())\n","    plt.ylim(yy.min(), yy.max())\n","\n","    # Plot\n","    if savefig_fp:\n","        plt.savefig(savefig_fp, format='png')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Z7RorJHHxfW7","colab":{"base_uri":"https://localhost:8080/","height":336},"executionInfo":{"status":"ok","timestamp":1608246148536,"user_tz":420,"elapsed":3620,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"7089f1ce-81ed-4003-bcba-a6457f9258e5"},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"7Cy3efR7s7HD"},"source":["# PyTorch"]},{"cell_type":"markdown","metadata":{"id":"hsnnH37eYxql"},"source":["Now that we've implemented logistic regression with Numpy, let's do the same with PyTorch. "]},{"cell_type":"code","metadata":{"id":"mWEfPoXzYxym"},"source":["import torch"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jVS_h2-cZW6U","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246151464,"user_tz":420,"elapsed":6531,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"1d2426ad-977f-4ba5-990e-8e58359bbeca"},"source":["# Set seed for reproducibility\n","torch.manual_seed(SEED)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<torch._C.Generator at 0x7f2549940138>"]},"metadata":{"tags":[]},"execution_count":39}]},{"cell_type":"markdown","metadata":{"id":"-h-dlCTaYowa"},"source":["## Model"]},{"cell_type":"markdown","metadata":{"id":"CuGUAtHkyFBv"},"source":["We will be using [Linear layers](https://pytorch.org/docs/stable/nn.html#linear-layers)  to recreate the same model."]},{"cell_type":"code","metadata":{"id":"7-vm9AZm1_f9"},"source":["from torch import nn\n","import torch.nn.functional as F\n","from torchsummary import summary"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"p63Ws2STYolf"},"source":["class LogisticRegression(nn.Module):\n","    def __init__(self, input_dim, num_classes):\n","        super(LogisticRegression, self).__init__()\n","        self.fc1 = nn.Linear(input_dim, num_classes)\n","        \n","    def forward(self, x_in, apply_softmax=False):\n","        y_pred = self.fc1(x_in)\n","        if apply_softmax:\n","            y_pred = F.softmax(y_pred, dim=1) \n","        return y_pred"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6kJgaEqIcyHR","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246151466,"user_tz":420,"elapsed":6516,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"a6437bac-1048-4fbc-f34d-6d4d403d53ee"},"source":["# Initialize model\n","model = LogisticRegression(input_dim=INPUT_DIM, num_classes=NUM_CLASSES)\n","print (model.named_parameters)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["<bound method Module.named_parameters of LogisticRegression(\n","  (fc1): Linear(in_features=2, out_features=2, bias=True)\n",")>\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"alKyigoqa9w9"},"source":["## Loss"]},{"cell_type":"markdown","metadata":{"id":"NLkVlnQJaQ4q"},"source":["Our loss will be the categorical [crossentropy](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss). "]},{"cell_type":"code","metadata":{"id":"wux5LwEGbFI7","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246151467,"user_tz":420,"elapsed":6504,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"331fd171-e3c7-4934-f02d-02263b1d9ce2"},"source":["loss_fn = nn.CrossEntropyLoss()\n","y_pred = torch.randn(3, NUM_CLASSES, requires_grad=False)\n","y_true = torch.empty(3, dtype=torch.long).random_(NUM_CLASSES)\n","print (y_true)\n","loss = loss_fn(y_pred, y_true)\n","print(f'Loss: {loss.numpy()}')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["tensor([0, 0, 0])\n","Loss: 1.2779091596603394\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"fl5kYzXVcahy"},"source":["In our case, we will also incorporate the class weights into our loss function to counter any class imbalances."]},{"cell_type":"code","metadata":{"id":"4gsEd7Mccarr"},"source":["# Define Loss\n","class_weights_tensor = torch.Tensor(list(class_weights.values()))\n","loss_fn = nn.CrossEntropyLoss(weight=class_weights_tensor)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"f9oyIzgccYLy"},"source":["## Metrics"]},{"cell_type":"markdown","metadata":{"id":"3bb9eYXXUYVC"},"source":["We'll compute accuracy as we train our model because just looking the loss value isn't super intuitive to look at. We'll look at other metrics (precision, recall, f1) in the evaluation section below."]},{"cell_type":"code","metadata":{"id":"rwzXNiPZcaCU"},"source":["# Accuracy\n","def accuracy_fn(y_pred, y_true):\n","    n_correct = torch.eq(y_pred, y_true).sum().item()\n","    accuracy = (n_correct / len(y_pred)) * 100\n","    return accuracy"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"94eG68-OzrWt","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246151468,"user_tz":420,"elapsed":6489,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"0d2d83db-f28b-4db0-9ad5-95dfdbea2337"},"source":["y_pred = torch.Tensor([0, 0, 1])\n","y_true = torch.Tensor([1, 1, 1])\n","print(f'Accuracy: {accuracy_fn(y_pred, y_true):.1f}')"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Accuracy: 33.3\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"m2zZs70qaOWn"},"source":["## Optimizer"]},{"cell_type":"markdown","metadata":{"id":"e6b4SDgjUadx"},"source":["We'll be sticking with our Adam optimizer from previous lessons."]},{"cell_type":"code","metadata":{"id":"DWeLpGaIa64l"},"source":["from torch.optim import Adam"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"oqzC6hRla7FN"},"source":["# Optimizer\n","optimizer = Adam(model.parameters(), lr=LEARNING_RATE) "],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"BTjMYiOQcqNb"},"source":["## Training"]},{"cell_type":"code","metadata":{"id":"1X9BaZWwdD0P"},"source":["# Convert data to tensors\n","X_train = torch.Tensor(X_train)\n","y_train = torch.LongTensor(y_train)\n","X_val = torch.Tensor(X_val)\n","y_val = torch.LongTensor(y_val)\n","X_test = torch.Tensor(X_test)\n","y_test = torch.LongTensor(y_test)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"hA7Oz97NAe8A","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246151470,"user_tz":420,"elapsed":6471,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"ab19931f-9ba0-4720-c8fe-119c4bc81250"},"source":["# Training\n","for epoch in range(NUM_EPOCHS):\n","    # Forward pass\n","    y_pred = model(X_train)\n","\n","    # Loss\n","    loss = loss_fn(y_pred, y_train)\n","\n","    # Zero all gradients\n","    optimizer.zero_grad()\n","\n","    # Backward pass\n","    loss.backward()\n","\n","    # Update weights\n","    optimizer.step()\n","\n","    if epoch%10==0: \n","        predictions = y_pred.max(dim=1)[1] # class\n","        accuracy = accuracy_fn(y_pred=predictions, y_true=y_train)\n","        print (f\"Epoch: {epoch} | loss: {loss:.2f}, accuracy: {accuracy:.1f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Epoch: 0 | loss: 0.96, accuracy: 60.7\n","Epoch: 10 | loss: 0.28, accuracy: 87.0\n","Epoch: 20 | loss: 0.15, accuracy: 95.6\n","Epoch: 30 | loss: 0.12, accuracy: 97.7\n","Epoch: 40 | loss: 0.10, accuracy: 98.3\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"dM7iYW8ANYjy"},"source":["## Evaluation"]},{"cell_type":"markdown","metadata":{"id":"rEcS6Qw5VA-i"},"source":["First let's see the accuracy of our model on our test split."]},{"cell_type":"code","metadata":{"id":"oGx8sCGMVbrv"},"source":["from sklearn.metrics import accuracy_score"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-jZtTd7F6_ps","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246151472,"user_tz":420,"elapsed":6459,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"e6f7430c-7bfc-4c13-e88c-25ae26b12c27"},"source":["# Predictions\n","pred_train = model(X_train, apply_softmax=True)\n","pred_test = model(X_test, apply_softmax=True)\n","print (f\"sample probability: {pred_test[0]}\")\n","pred_train = pred_train.max(dim=1)[1]\n","pred_test = pred_test.max(dim=1)[1]\n","print (f\"sample class: {pred_test[0]}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["sample probability: tensor([0.0449, 0.9551], grad_fn=<SelectBackward>)\n","sample class: 1\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"sEjansj78Rqe","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246151803,"user_tz":420,"elapsed":6778,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"1d7f7d81-b7e7-49d7-dfb7-11c5a23ff253"},"source":["# Accuracy (could've also used our own accuracy function)\n","train_acc = accuracy_score(y_train, pred_train)\n","test_acc = accuracy_score(y_test, pred_test)\n","print (f\"train acc: {train_acc:.2f}, test acc: {test_acc:.2f}\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["train acc: 0.98, test acc: 0.98\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"LUpnux8DVqnV"},"source":["We can also evaluate our model on other meaningful metrics such as precision and recall. These are especially useful when there is data imbalance present."]},{"cell_type":"markdown","metadata":{"id":"80MwyE0yOr-k"},"source":["$\\text{accuracy} = \\frac{TP+TN}{TP+TN+FP+FN}$ \n","\n","$\\text{recall} = \\frac{TP}{TP+FN}$ → (how many of the actual issues did I catch)\n","\n","$\\text{precision} = \\frac{TP}{TP+FP}$ → (out of all the things I said were issues, how many were actually issues)\n","\n","$F_1 = 2 * \\frac{\\text{precision }  *  \\text{ recall}}{\\text{precision } + \\text{ recall}}$\n","\n","where: \n","* TP: # of samples truly predicted to be positive and were positive\n","* TN: # of samples truly predicted to be negative and were negative\n","* FP: # of samples falsely predicted to be positive but were negative\n","* FN: # of samples falsely predicted to be negative but were positive"]},{"cell_type":"code","metadata":{"id":"ClkXhEZcVqwV"},"source":["import json\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import precision_recall_fscore_support"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"5NzU9m7HWgh7"},"source":["def get_performance(y_true, y_pred, classes):\n","    \"\"\"Per-class performance metrics.\"\"\"\n","    # Performance\n","    performance = {\"overall\": {}, \"class\": {}}\n","\n","    # Overall performance\n","    metrics = precision_recall_fscore_support(y_true, y_pred, average=\"weighted\")\n","    performance[\"overall\"][\"precision\"] = metrics[0]\n","    performance[\"overall\"][\"recall\"] = metrics[1]\n","    performance[\"overall\"][\"f1\"] = metrics[2]\n","    performance[\"overall\"][\"num_samples\"] = np.float64(len(y_true))\n","\n","    # Per-class performance\n","    metrics = precision_recall_fscore_support(y_true, y_pred, average=None)\n","    for i in range(len(classes)):\n","        performance[\"class\"][classes[i]] = {\n","            \"precision\": metrics[0][i],\n","            \"recall\": metrics[1][i],\n","            \"f1\": metrics[2][i],\n","            \"num_samples\": np.float64(metrics[3][i]),\n","        }\n","\n","    return performance"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8N8jPq_Cy2hp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246151805,"user_tz":420,"elapsed":6763,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"722d2c68-372e-4e2b-d41c-bb6d81dd3d33"},"source":["# Performance report\n","performance = get_performance(y_true=y_test, y_pred=pred_test, classes=label_encoder.classes)\n","print (json.dumps(performance, indent=2))"],"execution_count":null,"outputs":[{"output_type":"stream","text":["{\n","  \"overall\": {\n","    \"precision\": 0.9804753820033956,\n","    \"recall\": 0.9773238380809595,\n","    \"f1\": 0.9788484136310223,\n","    \"num_samples\": 150.0\n","  },\n","  \"class\": {\n","    \"benign\": {\n","      \"precision\": 0.9824561403508771,\n","      \"recall\": 0.9655172413793104,\n","      \"f1\": 0.9739130434782608,\n","      \"num_samples\": 58.0\n","    },\n","    \"malignant\": {\n","      \"precision\": 0.978494623655914,\n","      \"recall\": 0.9891304347826086,\n","      \"f1\": 0.9837837837837837,\n","      \"num_samples\": 92.0\n","    }\n","  }\n","}\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"MmEzXIiJW5tJ"},"source":["With logistic regression (extension of linear regression), the model creates a linear decision boundary that we can easily visualize."]},{"cell_type":"code","metadata":{"id":"CnjfgWu9W43L"},"source":["def plot_multiclass_decision_boundary(model, X, y):\n","    x_min, x_max = X[:, 0].min() - 0.1, X[:, 0].max() + 0.1\n","    y_min, y_max = X[:, 1].min() - 0.1, X[:, 1].max() + 0.1\n","    xx, yy = np.meshgrid(np.linspace(x_min, x_max, 101), np.linspace(y_min, y_max, 101))\n","    cmap = plt.cm.Spectral\n","    \n","    X_test = torch.from_numpy(np.c_[xx.ravel(), yy.ravel()]).float()\n","    y_pred = model(X_test, apply_softmax=True)\n","    _, y_pred = y_pred.max(dim=1)\n","    y_pred = y_pred.reshape(xx.shape)\n","    plt.contourf(xx, yy, y_pred, cmap=plt.cm.Spectral, alpha=0.8)\n","    plt.scatter(X[:, 0], X[:, 1], c=y, s=40, cmap=plt.cm.RdYlBu)\n","    plt.xlim(xx.min(), xx.max())\n","    plt.ylim(yy.min(), yy.max())"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":336},"id":"PR2_G8mBW48k","executionInfo":{"status":"ok","timestamp":1608246152639,"user_tz":420,"elapsed":7583,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"f41f001f-bd82-44e2-ad5a-07bc94dfed18"},"source":["# Visualize the decision boundary\n","plt.figure(figsize=(12,5))\n","plt.subplot(1, 2, 1)\n","plt.title(\"Train\")\n","plot_multiclass_decision_boundary(model=model, X=X_train, y=y_train)\n","plt.subplot(1, 2, 2)\n","plt.title(\"Test\")\n","plot_multiclass_decision_boundary(model=model, X=X_test, y=y_test)\n","plt.show()"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 864x360 with 2 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"9v6zc1_1PWnz"},"source":["## Inference"]},{"cell_type":"code","metadata":{"id":"kX9428-EPUzx","colab":{"base_uri":"https://localhost:8080/","height":80},"executionInfo":{"status":"ok","timestamp":1608246288801,"user_tz":420,"elapsed":969,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"983b0494-6b21-4f1a-9236-a03428ce8798"},"source":["# Inputs for inference\n","X_infer = pd.DataFrame([{'leukocyte_count': 13, 'blood_pressure': 12}])\n","X_infer.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>leukocyte_count</th>\n","      <th>blood_pressure</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>13</td>\n","      <td>12</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   leukocyte_count  blood_pressure\n","0               13              12"]},"metadata":{"tags":[]},"execution_count":64}]},{"cell_type":"code","metadata":{"id":"3qY6buxLKb2o","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246152640,"user_tz":420,"elapsed":7559,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"e2c9d291-a9f6-4f41-c17a-1f88c105ffd3"},"source":["# Standardize\n","X_infer = X_scaler.transform(X_infer)\n","print (X_infer)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[-0.68505296 -3.11487099]]\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"7O5PbOAvXTzF","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608246238161,"user_tz":420,"elapsed":762,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"371dfff9-df29-4097-b7dc-6963287788d7"},"source":["# Predict\n","y_infer = model(torch.Tensor(X_infer), apply_softmax=True)\n","prob, _class = y_infer.max(dim=1)\n","label = label_encoder.decode(_class.detach().numpy())[0]\n","print (f\"The probability that you have a {label} tumor is {prob.detach().numpy()[0]*100.0:.0f}%\")"],"execution_count":null,"outputs":[{"output_type":"stream","text":["The probability that you have a benign tumor is 93%\n"],"name":"stdout"}]},{"cell_type":"markdown","metadata":{"id":"8PLPFFP67tvL"},"source":["# Unscaled weights"]},{"cell_type":"markdown","metadata":{"id":"_XYBW85WIdKy"},"source":["Note that only X was standardized.\n","\n","$\\hat{y}_{unscaled} = b_{scaled} + \\sum_{j=1}^{k}W_{{scaled}_j}x_{{scaled}_j}$\n","* $x_{scaled} = \\frac{x_j - \\bar{x}_j}{\\sigma_j}$\n","\n","$\\hat{y}_{unscaled} = b_{scaled} + \\sum_{j=1}^{k} W_{{scaled}_j} (\\frac{x_j - \\bar{x}_j}{\\sigma_j}) $\n","\n","$\\hat{y}_{unscaled} = (b_{scaled} - \\sum_{j=1}^{k} W_{{scaled}_j}\\frac{\\bar{x}_j}{\\sigma_j}) +  \\sum_{j=1}^{k} (\\frac{W_{{scaled}_j}}{\\sigma_j})x_j$\n","\n","In the expression above, we can see the expression $\\hat{y}_{unscaled} = W_{unscaled}x + b_{unscaled} $, therefore:\n","\n","* $W_{unscaled} = \\sum_{j=1}^{k} (\\frac{W_{{scaled}_j}}{\\sigma_j}) $\n","\n","* $b_{unscaled} = b_{scaled} - \\sum_{j=1}^{k} W_{{scaled}_j}\\frac{\\bar{x}_j}{\\sigma_j}$"]},{"cell_type":"code","metadata":{"id":"KTSpxbwy7ugl","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1608144579635,"user_tz":420,"elapsed":437,"user":{"displayName":"Goku Mohandas","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14GjMIOf3R_zwS_zZx4ZyPMtQe0lOkGpPOEUEKWpM7g=s64","userId":"00378334517810298963"}},"outputId":"c2f31ef4-21e3-4d71-e6c1-a6164a5606d7"},"source":["# Unstandardize weights \n","W = model.fc1.weight.data.numpy()\n","b = model.fc1.bias.data.numpy()\n","W_unscaled = W / X_scaler.scale_\n","b_unscaled = b - np.sum((W_unscaled * X_scaler.mean_))\n","print (W_unscaled)\n","print (b_unscaled)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["[[ 0.61299698 -1.18550903]\n"," [-0.9537106   0.88432898]]\n","[ 8.929324 10.233843]\n"],"name":"stdout"}]}]}